Question 1

- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the
stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:
Does the solution meet the goal?

Accepted Answer

train_step is missing. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu

Question 2

- (Exam Topic 3)
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using
Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Accepted Answer

Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes *
np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0) Penalty parameter C of the error term. References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

Question 3

- (Exam Topic 3)
HOTSPOT

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You register the following versions of a model.
You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.
After the run has been submitted and completed, you run the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Accepted Answer

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

Question 4

- (Exam Topic 3)
You are producing a multiple linear regression model in Azure Machine Learning Studio. Several independent variables are highly correlated.
You need to select appropriate methods for conducting effective feature engineering on all the data.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the
correct order.

Accepted Answer

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Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores
from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selec
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

Question 5

- (Exam Topic 3)
You have a dataset that includes confidential data. You use the dataset to train a model.
You must use a differential privacy parameter to keep the data of individuals safe and private. You need to reduce the effect of user data on aggregated results.
What should you do?

Accepted Answer

Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known
as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private)
the data is.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

Question 6

- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the
stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to
compensate for the class imbalance. Solution: You use the Stratified split for the sampling mode.
Does the solution meet the goal?

Accepted Answer

Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number
of rare cases than simply duplicating existing cases.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

Question 7

- (Exam Topic 3)
You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.
You must use CUDA-based model training. You need to provision the Compute Instance.
Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area.
NOTE: Each correct selection is worth one point.

Accepted Answer

CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA
enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
Reference:
https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html

Question 8

- (Exam Topic 3)
You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry
script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.
What should you do?

Accepted Answer

How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine
Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In
this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks
are:
* 1. Register the model in the workspace model registry.
* 2. Define Inference Configuration:
* a. Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments.
* b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.
* 3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).

Question 9

- (Exam Topic 3)
You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model
training.
You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and
includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement. What should you do?

Accepted Answer

By default, the Web Service Input will expect the same data schema as the module output data which connects to the same downstream port as it. You can
remove the target variable column in the inference pipeline using Select Columns in Dataset module. Make sure that the output of Select Columns in Dataset
removing target variable column is connected to the same port as the output of the Web Service Intput module.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

Question 10

- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the
stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load
a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named amlcompute that references the target compute cluster.
Solution: Run the following code:
Does the solution meet the goal?

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Accepted Answer

The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which
can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder, compute_target=compute_target, entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn